YNUNLP at SemEval-2023 Task 2:The Pseudo Twin Tower Pre-training Model for Chinese Named Entity Recognition

Abstract

This paper introduces our method of developing a system for SemEval 2023 Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition, Track 9-Chinese. In this task, we need to identify entity boundaries and category labels for the six identified categories. The focus of this task is to detect fine-grained named entities whose data set has a fine-grained taxonomy of 36 NE classes, representing a realistic challenge for NER. We use BERT embedding to represent each character in the original sentence and train CRF-Rdrop to predict named entity categories using the data set provided by the organizer. Our best submission, with a macro average f1 score of 0.5657, ranked 15th out of 22 teams.